Groups are critical to human survival, allowing for greater collective access to resources and protection. Yet groups can also inflict harm, and miscategorizing the affect of a group can be costly. Judgments about whether a group is friendly or hostile are thus of great importance; they shape how people then interact with that group, regardless of whether those attributions are accurate. Since groups are more capable of inflicting harm than individuals, the potential cost of mislabeling a group's emotion should be greater than that of mislabeling the emotion of an individual. Extrapolating upon error management theory (EMT), we reasoned that it would be advantageous for people to engage a bias to categorize groups as "angry." Specifically, we expected that when making rapid decisions about groups' facial expressions, people would engage a bias to report the presence of anger. Additionally, we predicted that this bias would be amplified in the face of higher uncertainty about the group's emotion. Our results supported both of these predictions. Eighty-two observers viewed a single face or a group of 12 faces for 100-msec and indicated whether the face or faces were angry or happy. A signal detection analysis revealed that observers were biased to report the presence of anger. Importantly, this bias was amplified in response to groups compared to single faces. This was especially strong when the expressions of emotion were lowest in intensity, and therefore more ambiguous. Crucially, this bias occurred despite greater perceptual sensitivity to the actual expressions of groups compared to individuals, confirmed by modeling using probability summation. Our findings are consistent with EMT, which suggests that people may make biased attributions so that, if in error, they come at minimal cost. Compared to individuals, groups pose a compounded threat, and may therefore be more likely to be categorized as angry.